Systems, Not Slides: IBM’s Ray Bahari on AI-Driven Product Marketing and Launches That Learn

Summary

If your launches still run on “plan, ship, celebrate,” you’re leaving revenue on the table.

Ray Bahari, Global Product Marketing Lead at IBM and author of Systems, Not Slides: An AI Blueprint for Product Marketing, lays out how to move from launch theater to launch telemetry—compressing the distance between insight and execution.

Drawing on a career that spans scaling an early e‑commerce company, medtech at Stryker, mobile research at Polefish, insurance growth at Plymouth Rock, brand-building at JPMorgan’s Onyx, and ABM at IBM, Ray shares a practical playbook for adaptive positioning, sales enablement, and brand‑safe content at scale.

He explains how to build “minimum viable loops” across PMM—so positioning, proofs, messaging, and objections continuously inform each other—and how to choose a focused AI stack without drowning in tools.

Expect specifics on variant testing without losing brand consistency, creating a reusable bank of proofs, instrumenting journeys for speed-to-signal, and establishing a test‑and‑learn culture that actually sticks.

Timestamps

[00:45] – Guest intro and career arc: from early e‑commerce to SaaS, medtech, fintech, and IBM

[02:34] – Influencer dynamics in buying: lessons from Oticon and randomized rewards

[04:58] – Inside Stryker: digitizing the field with iPads and measurable customer interactions

[06:54] – Mobile-first research: Polefish and getting 2,000 survey responses overnight

[07:50] – Competing in insurance: partnerships, a homegrown DMP, and incrementality

[09:40] – IBM and JPMorgan: ABM impact, building Onyx, and returning to scale GTM with AI

[11:43] – From theater to telemetry: compression, adaptive positioning, and brand-safe variants

[17:16] – Tool choices, custom GPTs, and building Minimum Viable Loops and culture

Takeaways

- Build systems and loops—not one-off campaigns—to compress the time from insight to execution.

- Use AI to synthesize calls, CRM notes, and web behavior into objections, proofs, and enablement updates.

- Treat positioning as adaptive: test variants by segment and learn from “second-best” messages.

- Maintain brand consistency by training models on guidelines and anchoring content in a reusable bank of proofs.

- Curate a pragmatic AI stack: pick tools by task, leverage custom GPTs/LLMs, and document what works.

- Make test-and-learn cultural: start small, secure executive sponsorship, and run weekly operating rhythms.

Trust-First GTM: Fractional CMO Mariam Slimane on AI Enablement and Value‑Based Pricing

Summary

AI is flooding go-to-market with content and tools—but where does it actually move revenue and build trust?

Mariam Slimane, Fractional Chief Marketing Officer at CX Fractional Advisors with 15 years in B2B SaaS (DevOps and cybersecurity), shares a pragmatic playbook for applying AI where it counts.

She explains how AI is transforming enablement, training, market research, and call analysis—surfacing next-best actions so sales teams respond faster and smarter. Mariam is bullish on data hygiene, faster experimentation, and in-house AI co-pilots tailored to your workflows, and she’s candid about the risks of hyper-personalization and “AI theater.”

She breaks down a favorite revenue lever—AI-assisted value-based pricing and bundling—and why getting pricing right early builds credibility and reduces end-of-cycle discounting. Expect clear guidance on avoiding channel sprawl, doubling down on ICP clarity, investing in marketing ops alongside sales ops, and the specific AI skills leaders should learn now to unlock custom solutions and better data infrastructure.

Timestamps

[00:20] – Mariam’s background: B2B SaaS, DevOps/cybersecurity, and a scale-up mindset

[01:03] – Where AI truly helps GTM: enablement, call analysis, research, and next-best-action

[02:42] – Data hygiene and rapid experimentation; building org-specific AI co-pilots

[04:50] – Cautions: hyper-personalization limits, “AI theater,” and shiny-tool skepticism

[05:55] – Cutting through AI noise: authenticity, in-person touchpoints, and value over volume

[09:05] – Revenue tactic: AI-driven value-based pricing and bundling to stop leaving money on the table

[11:30] – Focus beats channel sprawl; know your ICP (e.g., why paid ads miss in public sector)

[13:53] – Ops and org design: invest in MOPs/SOPs, protect data, design from workflows and forecasts

Takeaways

- Clean your data to make AI reliable—bad inputs now create bigger, costlier mistakes.

- Use AI to power enablement and call analysis, surfacing tailored assets and responses for sellers.

- Build or customize AI co-pilots in-house to fit your workflows, products, and buyers.

- Lead with authenticity: let automation remove busywork, not human connection.

- Implement value-based pricing and bundling with AI-assisted analysis; align pricing with messaging to build trust early.

- Focus your channels around a precise ICP and strengthen the marketing–sales ops partnership to scale responsibly.

Earning B2B Trust in the AI Era: Credible GTM and Customer Expansion with Brightrose’s Kristin Elke

Summary

In a world flooded with AI-generated content, how do B2B startups earn real trust and stand out?

Kristin Elke, Managing Director at Brightrose and a 20+ year B2B tech marketer, breaks down how to balance AI efficiency with credibility. .

She shares a practical framework for building market trust through ecosystem advocacy—customers, partners, and analysts—paired with quantified proof (think time-to-value and measurable outcomes).

Kristin explains why modular, focus-driven GTM teams outperform one-size-fits-all hires, how to prune tool sprawl, and where AI can truly help—especially in customer expansion.

Expect specifics: turning happy customers into multi-format stories, focusing on segmentation and differentiated messaging, and using automated CS dashboards in place of over-engineered QBRs. Kristin also previews the evolution of her book, Trust, and why the Edelman Trust Barometer signals urgency for B2B leaders.

Timestamps

[00:59] – Kristin’s background and Brightrose’s “transform to perform” focus

[02:00] – AI’s real impact: first-draft speed, personalization, and the trust deficit (plus the productivity learning curve)

[05:57] – Startup credibility playbook: ecosystem advocacy, customer stories, and quantified proof/time-to-value

[09:48] – Focused GTM: segmentation, differentiated messaging, modular resourcing, and pruning tool sprawl

[12:37] – Beyond acquisition: AI tools for expansion and PLG-style upsell in enterprise accounts

[14:46] – Customer success pitfalls: over-rotating to new logos; automate health dashboards vs. constant QBRs

[17:37] – The evolving “trust” thesis and what’s coming in Kristin’s next book

[19:50] – Where to connect with Kristin and her parting advice: stay curious with AI—be diligent and specific

Takeaways

- Lead with proof: capture customer stories that include quantified outcomes (e.g., pipeline uplift, time-to-value) and third-party validation.

- Build ecosystem trust: mobilize customers, partners, and analysts—don’t rely on a single brand voice.

- Use AI to accelerate first drafts and personalization, then add strategy and POV to maintain credibility.

- Focus your GTM: segment ruthlessly, sharpen differentiated messaging, cut unused tools, and assemble modular talent to fill gaps.

- Prioritize expansion and retention: design scalable CS with automated dashboards and feedback loops; balance new-logo pursuit with upsell.

- Explore AI where it moves expansion metrics: integrate signals to spot upsell paths and enable efficient PLG motions in large accounts.

Beyond the Portal: Agentive AI, Human Trust, and 25–40% Revenue from Partnerships

Summary

Most partners don’t want to log into your portal—and that’s exactly where AI can change the game.

Vaughn Mordecai, Chief Revenue Officer at MindMatrix, explains how his team is turning partner enablement into an “AI operating system,” embedding intelligence so tasks like co-branding collateral or assembling sales rooms happen in the flow of work.

With 12+ years in channel, Vaughn gets specific on moving from bolted-on AI to agentive experiences, where an agent handles the busywork and surfaces what partners actually need—without killing human trust.

He shares the adoption traps that sink over half of AI programs (fear, irrelevance, “sideshow” pilots), and the blueprint to make AI stick: solve a real problem, keep humans at the center, and train teams to decide synchronously and fail fast. Expect practical examples, from MindMatrix’s “Skills in Seconds” microlearning to using NotebookLM and Gamma to speed proposals and decks, plus how sales-led data rooms personalize at scale.

Vaughn closes with a mandate for every GTM leader: make partnerships the fourth pillar alongside sales, marketing, and BD—because done right, they can drive 25–40% incremental revenue.

Timestamps

[00:45] – Vaughn’s path to CRO and MindMatrix’s partner management mission

[02:27] – AI as an operating system: embedding intelligence across GTM

[03:48] – From portals to agentive AI: enablement in the partner’s flow of work

[05:50] – Keep AI relevant: avoiding generic outreach and “AI vomit”

[08:24] – Cutting through the noise with human relationships and trust

[10:07] – Why AI fails inside orgs: fear, fit, and “OS vs. sideshow”

[12:47] – Synchronous decision-making: train to succeed or fail fast

[15:58] – Tools that work: Skills-in-Seconds, NotebookLM, Gamma, and sales data rooms

[22:00] – Parting advice: partnerships as the 4th GTM pillar driving 25–40% lift

Takeaways

- Embed AI as an operating system across partner workflows to eliminate portal friction.

- Deploy agentive AI to co-brand assets and surface next-best collateral on demand.

- Prioritize relevance: judge AI by how it helps partners and AMs, not by outputs alone.

- Keep humans in the loop—trust and relationships are what cut through market noise.

- Drive adoption by solving a real business problem, integrating AI into daily work, and training on data/process.

- Enable synchronous decisions: test quickly, learn, and scale or move on fast with microlearning and instant sales rooms.

AI-Powered GTM: Iris CRO on Guardrails, Stakeholders, and the DEA Playbook

Summary

AI is transforming go-to-market, but without the right guardrails and operators, it creates more noise than revenue.

Paul Quintal, Chief Revenue Officer at Iris, a global identity and cyber protection provider, shares how he builds a compliant, AI-enabled GTM engine while keeping the human edge.

With 20+ years leading revenue at companies from startup to IPO/acquisition, Paul explains where AI delivers real value today—signal gathering, knowledge-base assisted support, and hyper-personalized outreach—and where interoperability still lags. He walks through Iris’s vertical market strategy (TAM/SAM/SOM), the compliance hurdles of tools like ZoomInfo across US and GDPR, and how to map the true buying committee beyond the CISO to Product and Finance.

Paul also details his operating frameworks—DEA (Discipline, Execution, Accountability) and DOORS (Deliver on time, on budget, with referencable clients)—plus practical tactics for standing out in an AI-saturated inbox: permission-based, six-sentence emails, and yes, picking up the phone.

Expect candid, operator-level advice on turning AI signals into pipeline without abandoning the fundamentals.

Timestamps

[00:21] – Paul’s background: from dot-com sales to CRO/operator across IPOs and acquisitions

[02:00] – AI in GTM: real use cases, limits, and why guardrails/interoperability matter

[07:20] – When breaches hit: AI-assisted knowledge bases to stabilize call centers and NPS

[08:50] – Personalization at scale: custom outreach that clears spam and earns replies

[11:46] – Building a compliant GTM engine: ZoomInfo, GDPR hurdles, and vertical TAM/SAM/SOM

[15:23] – The DEA playbook: discipline, execution, accountability—and why the phone still wins

[16:42] – Find the right “pew”: mapping decision makers across CISO, Product, and Finance

[22:38] – DOORS framework: deliver on time, on budget, with referencable clients

Takeaways

- Establish AI guardrails and integrations across CRM and support tools to turn signals into workflows.

- Build vertical hunt lists with TAM/SAM/SOM; use AI for sequencing, but drive outcomes with live calling.

- Personalize outreach: ask permission, keep emails to six sentences, and avoid generic LinkedIn requests.

- Multi-thread deals: include CISO/CIO, Product (API integration), and Finance (revenue impact) in the buying group.

- Prepare for breach-driven volume spikes with AI-assisted knowledge bases to protect NPS and response times.

- Operationalize with frameworks: enforce DEA and DOORS to align activity, quality, and credibility at scale.

Orchestrating AI in GTM: Shawn D. Green on Agentic AI, Vertical Focus, and the New RevOps

Summary

AI is rewriting go-to-market faster than most leaders can absorb—so where do you start?

Shawn D. Green, a veteran operator turned GTM advisor to 12+ companies across fintech, supply chain, and AI, lays out a practical path. With 25 years in tech (NetSuite—acquired by Oracle, BlackLine pre-IPO) and a recent Series A build-and-sale, Shawn blends deep operator chops with hands-on AI work, including an MIT program and building seven bots himself. He explains how to pilot AI with discipline—pick 1–3 high-impact use cases, assign an owner and timeline, and scale what works—while moving from dashboards to true AI orchestration.

Shawn breaks down the market (horizontal vs. vertical vs. enterprise AI), why intent-led, 24/7 engines are changing outbound, and how agentic AI pushes work to 99% so humans decide.

He also maps the org changes ahead, from repurposing headcount to RevOps evolving into GTM engineering, plus his daily upskilling routine, the “three rocks” focus, and how to use design partners to find real value. He closes with a simple mandate: don’t freeze—stay curious and build.

Timestamps

[00:45] – Guest intro and career highlights: NetSuite/Oracle, BlackLine, advisor across 12+ companies

[03:02] – AI’s impact on GTM and how to pilot: 1–3 use cases, ownership, timelines

[04:58] – From reporting to AI orchestration; why leaders must lean in now

[07:39] – Horizontal vs. vertical vs. enterprise AI: likely winners and exit paths

[10:08] – Outbound reimagined: 24/7 intent engines and hotter, more qualified leads

[12:04] – Agentic AI, explained: bots to 99%, humans make the call

[13:46] – Upskilling fast: daily practice and building bots as a non-technical leader

[19:05] – RevOps becomes GTM engineering: org design and headcount shifts

Takeaways

- Orchestrate AI with focus: select 1–3 high-value use cases, assign an owner, set a timeline, then scale.

- Narrow your bet: vertical or enterprise AI positions are more durable than “me-too” horizontals.

- Automate top-of-funnel: use AI to surface high-intent buyers so reps spend time partnering, not prospecting.

- Redesign roles: repurpose headcount for higher-value work; evolve RevOps into GTM engineering to coordinate tools, data, and agents.

- Build an AI habit: invest 30–45 minutes daily; prototype bots even if you’re non-technical.

- Use design partners to validate value, iterate quickly, and prove ROI before wide rollout.

Drowning in AI hype while trying to build real pipeline? This episode cuts through the noise.

David Boyle—co-founder and Managing Partner at RevCentric Partners, two-time CRO with exits, and a seven-time world #1 rep—shares what actually moves revenue.

A veteran of PTC, BMC, and Zscaler, David helped author and deploy MEDDIC at PTC and now advises early- to mid-stage companies on proven sales execution. He breaks down five practical AI use cases that boost seller productivity in research, outreach, and meeting prep—plus where AI agents are showing lift (first meetings) and where they aren’t yet (revenue).

David also outlines the three recurring growth blockers he sees across companies: hiring rigor, creation of qualified opportunities, and middle-to-lower funnel control. You’ll hear concrete hiring benchmarks (<20 business days, <5 interviewers, ~25% first-interview-to-offer) and a simple outbound math model that can 4x qualified pipeline.

He illustrates why complex enterprise cycles adopt AI more slowly with a 213-meeting Goldman Sachs deal—and closes with how RevCentric’s “teach in the trenches” approach drives real adoption.

Timestamps

[00:45] – Guest intro: career highlights, RevCentric, and MEDDIC origins at PTC

[02:30] – Cutting through AI hype: five sales use cases and real productivity gains

[04:46] – How AI reshapes GTM: orchestration, headcount questions, and changing seller skills

[07:22] – AI agents today: booking meetings vs. moving revenue

[08:25] – Adoption varies by complexity: enterprise vs. high-velocity sales; the 213-meeting deal

[12:47] – Three growth blockers: hiring, creating qualified opps, and lower-funnel control

[15:56] – Hiring discipline for SMBs: speed, simplicity, and clear accountability

[18:49] – Outbound funnel math: doubling inputs and conversions to 4x qualified pipeline

Takeaways

- Ground your AI strategy in defined sales use cases—research, outreach, and meeting prep that keep reps on-message.

- Train sellers to orchestrate systems while doubling down on fundamentals: champions, economic buyer access, and forecast control.

- Set hiring guardrails: under 20 business days, fewer than five interviewers, and ~25% first-interview-to-offer without lowering the bar.

- Assign clear hiring accountability to the hiring manager; avoid “committee” decisions that dilute ownership.

- Measure and coach outbound rigor: increase weekly seller-set meetings and raise first-meeting-to-qualified conversion.

- Do the funnel math—small gains compound; doubling meetings and conversion can 4x qualified opportunities.

AI That Actually Works for Small Business: Practical AIGC, DIY Hosting, and Prompting Playbooks

Summary

Want AI to save time and money — not create more complexity?

Steve Gonzalez, Senior Technical Program Manager at Washington Digital Media and fractional CMO, lays out practical ways small businesses can adopt AI without getting trapped in endless SaaS subscriptions.

Steve blends hands-on tech (hosting on a NAS, Cloudflare protection, Google Calendar integrations) with creative marketing: AI-generated UGC, hybrid real/AI content, and case studies like Dudley Beauty School.

He also covers the near-future of VR/Web3, data/licensing issues around faces and content, and concrete prompting and MVP workflows that deliver scalable ROI.

Listen for step-by-step tactics to reduce costs, run better experiments, and get predictable results from AI.

Timestamps

[00:22] – Guest intro: Steve’s background and mission helping creatives adopt tech  

[01:47] – How AI is reshaping marketing and closing time/budget gaps  

[03:14] – Tool scouting: consistency, quality, and avoiding subscription bloat  

[05:24] – DIY digital architecture: booking, hosting on a NAS, Cloudflare example (whenisteveavailable.com)  

[06:56] – The next frontier: VR/Web3 commercial opportunities for small businesses  

[09:33] – Case study: Dudley Beauty School — mixing AIGC + real UGC across the funnel  

[12:36] – Rights, licensing, and the ethics of using faces and AI-generated likenesses  

[17:50] – Prompting & build strategy: ask first, breadcrumb features, and iterate with MVPs

Takeaways

- Audit and simplify your stack: replace recurring SaaS where a lightweight self-hosted or open-source setup suffices.  

- Start with a clear end goal: reverse-engineer outcomes before prompting an LLM.  

- Prompt strategically: answer who/what/when/where/why, speak your prompt, then refine incrementally.  

- Build incrementally: ship an MVP, then layer features and different AI tools to scale.  

- Isolate AI contexts: use separate AI accounts or instances per client to avoid data bleed and get tailored outputs.  

- Measure ROI early: run rapid experiments, track outcomes (time + money), and scale only the winners.

From SEO to GEO: CRO Robert Kazmi on AI-Native Marketing and Line of Sight

Summary

Your buyers aren’t Googling first anymore—they’re asking ChatGPT, Claude, and Grok. In this episode, Robert Kazmi, Chief Revenue Officer at Koombea, an AI-driven mobile and web app development partner, explains how go-to-market teams must evolve from search engine optimization (SEO) to generative engine optimization (GEO). Robert shares how AI is reshaping buyer expectations, compressing research cycles, and rewarding brands that add real value to LLMs’ source ecosystems. He details how his team rebuilds content pillars for GEO, uses AI to triage support vs. human engagement, and even treats LLMs as indirect lead sources by understanding the data they consume. Robert also breaks down developer–AI interaction—why hallucinations demand human audit, how prompt engineering becomes a core skill, and what “line of sight” leadership looks like to align metrics with outcomes. Expect practical insights on standing out amid AI noise, compressing trust-building to seconds, and preparing teams for an AI-native GTM.

Timestamps

[00:45] – Guest intro and Koombea's AI-native app focus  

[01:20] – AI reshaping buyer expectations; productivity, self-serve, and marketing triage  

[03:45] – From SEO to GEO: getting found inside ChatGPT, Claude, and Grok  

[05:50] – Rebuilding content pillars for GEO and driving high-intent leads  

[08:40] – Turning LLMs into indirect lead sources: data-source strategy  

[12:20] – Developer–AI interaction: hallucinations, context, and prompt engineering  

[17:20] – Can AI agents sell? Filters, noise, and compressing trust to a five-second search  

[32:05] – Operating with “line of sight”: metrics that matter, alignment, and execution

Takeaways

- Reorient from SEO to GEO: design content for LLM retrieval—specific, expert, and genuinely useful.  

- Seed value where LLMs look: publish proof-rich assets in high-weight sources (e.g., forums, expert hubs, analyst content).  

- Use AI to triage and scale: route simple requests to self-serve and reserve humans for complexity; let AI accelerate content pillars and campaigns.  

- Build human guardrails: upskill teams in prompt engineering and implement audit workflows to catch hallucinations.  

- Break through AI noise: lead with creativity, clear outcomes, and social proof to earn trust fast.  

- Lead with “line of sight”: align teams on objectives and measurable business metrics—not vanity numbers

From Creator to Editor: Rehumanizing AI Marketing with Lace Partners’ Chris Howard

Summary

AI lets marketers move faster than ever—but brand trust is on the line. Chris Howard, Executive Director and Head of Marketing at Lace Partners, shares how his team acts as brand custodians in an AI-first world, rehumanizing content so it reflects who they are—not a generic consultancy. Chris explains why marketers must shift from “content creators” to “editors,” the core skills he now hires for (agility and precise prompting), and how he cut content turnaround from days to hours. He details a practical analytics win using Excel and Microsoft Copilot to decode LinkedIn performance at scale—then feed those insights back into language, format, and channel strategy. You’ll hear how Lace is redesigning content for AEO (answer engine optimization), elevating short-form video quality, and selecting tools without getting trapped by vendor churn. Chris also reframes AI as a “teammate” embedded across workflows—especially in HR—while addressing creative opportunities (Synthesia, Descript) and risks around identity and security. He closes with candid advice: experiment more, kill what’s not working, and double down on short-form video.

Timestamps

[00:45] – Guest intro: Lace Partners, remit, and marketing’s scope

[03:14] – Brand custodianship and “rehumanizing” AI-generated content

[06:43] – Skills for modern marketers: agility and precision prompting

[09:04] – Copilot + Excel: turning LinkedIn data into content and language patterns

[11:05] – Campaign design: AEO vs. SEO, FAQs, and upgrading short-form video

[13:53] – Biggest GTM challenge: keeping up with tool velocity and fit

[15:28] – Practical stack: Copilot, HubSpot AI agents, Riverside, Descript, Synthesia

[17:40] – AI as a teammate: embedding agents across HR and marketing

Takeaways

- Rehumanize AI output—treat brand as a craft and keep humans as final editors.

- Train for agility and master prompting to cut cycle times without losing quality.

- Let analytics lead creativity—use AI to mine platform data for language and format cues.

- Optimize for AEO—structure content for question-based discovery and FAQs.

- Professionalize short-form video—raise production value while staying fast.

- Treat AI as a teammate—embed agents in workflows and set guardrails for identity and security.